Medical Image Computing and Computer Assisted Intervention - MICCAI 2021: 24th International Conference, Strasbourg, France, September 27 - October 1,, de Bruijne Marleen, Cattin Philippe C., Cotin Stйphane
Описание: This book constitutes the proceedings of the Second International Workshop on Advances in Simplifying Medical UltraSound, ASMUS 2021, held on September 27, 2021, in conjunction with MICCAI 2021, the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention.
Описание: Contrastive Representations for Continual Learning of Fine-grained Histology Images.- Learning Transferable 3D-CNN for MRI-based Brain Disorder Classification from Scratch: An Empirical Study.- Knee Cartilages Segmentation Based on Multi-scale Cascaded Neural Networks.- Deep PET/CT fusion with Dempster-Shafer theory for lymphoma segmentation.- Interpretable Histopathology Image Diagnosis via Whole Tissue Slide Level Supervision.- Variational Encoding and Decoding for Hybrid Supervision of Registration Network.- Multiresolution Registration Network (MRN) Hierarchy with Prior Knowledge Learning.- Learning to Synthesize 7T MRI from 3T MRI with Few Data by Deformable Augmentation.- Rethinking Pulmonary Nodule Detection in Multi-view 3D CT Point Cloud Representation.- End-to-end lung nodule detection framework with model-based feature projection block.- Learning Structure from Visual SemanticFeatures and Radiology Ontology for LymphNode Classification on MRI.- Improving Joint Learning of Chest X-Ray and Radiology Report by Word Region Alignment.- Cell Counting by a Location-Aware Network.- Exploring Gyro-Sulcal Functional Connectivity Differences across Task Domains via Anatomy-Guided Spatio-Temporal Graph Convolutional Networks.- StairwayGraphNet for Inter- and Intra-modality Multi-resolution Brain Graph Alignment and Synthesis.- Multi-Feature Semi-Supervised Learning for COVID-19 Diagnosis from Chest X-ray Images.- Transfer learning with a layer dependent regularization for medical image segmentation.- Multi-Scale Self-Supervised Learning for Multi-Site Pediatric Brain MR Image Segmentation with Motion/Gibbs Artifacts.- Deep active learning for dual-view mammogram analysis.- Statistical Dependency Guided Contrastive Learning for Multiple Labeling in Prenatal Ultrasound.- Semi-supervised Learning Regularized by Adversarial Perturbation and Diversity Maximization.- TransforMesh: A Transformer Network for Longitudinal Modeling of Anatomical Meshes.- A Recurrent Two-stage Anatomy-guided Network for Registration of Liver DCE-MRI.- Learning Infancy Brain Developmental Connectivity for the Cognitive Score Prediction.- Hierarchical 3D Feature Learning for Pancreas Segmentation.- Voxel-wise Cross-Volume Representation Learning for 3D Neuron Reconstruction.- Diagnosis of Hippocampal Sclerosis from Clinical Routine Head MR Images using Structure-Constrained Super-Resolution Network.- U-Net Transformer: Self and Cross Attention for Medical Image Segmentation.- Pre-biopsy multi-class classification of breast lesion pathology in mammograms.- Co-Segmentation of Multi-Modality Spinal Images Using Channel and Spatial Attention.- Hetero-Modal Learning and Expansive Consistency Constraints for Semi-Supervised Detection from Multi-Sequence Data.- STRUDEL: Self-Training with Uncertainty Dependent Label Refinement across Domains.- Deep Reinforcement Learning for L3 Slice Localization in Sarcopenia Assessment.- MIST GAN: Modality Imputation using Style Transfer for MRI.- Biased Extrapolation in Latent Space for Imbalanced Deep Learning.- 3DMeT: 3D Medical Image Transformer for Knee Cartilage Defect Assessment.- A Gaussian Process Model for Unsupervised Analysis of High Dimensional Shape Data.- Standardized Analysis of Kidney Ultrasound Images for the Prediction of Pediatric Hydronephrosis Severity.- Automated deep learning-based detection of osteoporotic fractures in CT images.- GT U-Net: A U-Net Like Group Transformer Network for Tooth Root Segmentation.- Information Bottleneck Attribution for Visual Explanations of Diagnosis and Prognosis.- Stacked Hourglass Network with a Multi-level Attention Mechanism: Where to Look for Intervertebral Disc Labeling.- TED-net: Convolution-free T2T Vision Transformer-based Encoder-decoder Dilation network for Low-dose CT Denoising.- Self-supervised Mean Teacher for Semi-supervisedChest X-ray Classification.- VoxelEmbed: 3D Instance Segmentation and Tracking with Voxel Embedding based Deep Learning.
Описание: This book constitutes the Second Automatization of Cranial Implant Design in Cranioplasty Challenge, AutoImplant 2021, which was held in conjunction with the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, in Strasbourg, France, in September, 2021.
Описание: Adjacent Scale Fusion and Corneal Position Embedding for Corneal Ulcer Segmentation.- Longitudinal detection of diabetic retinopathy early severity grade changes using deep learning.- Intra-operative OCT (iOCT) Image Quality Enhancement: A Super-Resolution Approach using High Quality iOCT 3D Scans.- Diabetic Retinopathy Detection based on Weakly Supervised Object Localization and Knowledge Driven Attribute Mining.- FARGO: A Joint Framework for FAZ and RV Segmentation from OCTA Images.- CDLRS: Collaborative Deep Learning Model with Joint Regression and Segmentation for Automatic Fovea Localization.- U-Net with Hierarchical Bottleneck Attention for Landmark Detection in Fundus Images of the Degenerated Retina.- Radial U-Net: Improving DMEK Graft Detachment Segmentation in Radial AS-OCT Scans.- Guided Adversarial Adaptation Network for Retinal and Choroidal Layer Segmentation.- Juvenile Refractive Power Prediction based on Corneal Curvature and Axial Length via a Domain Knowledge Embedding Network.- Peripapillary Atrophy Segmentation with Boundary Guidance.- Are cardiovascular risk scores from genome and retinal image complementary? A deep learning investigation in a diabetic cohort.- Dual-branch Attention Network and Atrous Spatial Pyramid Pooling for Diabetic Retinopathy Classification Using Ultra-Widefield Images.- Self-Adaptive Transfer Learning for Multicenter Glaucoma Classification in Fundus Retina Images.- Multi-Modality Images Analysis: A Baseline for Glaucoma Grading via Deep Learning.- Impact of data augmentation on retinal OCT image segmentation for diabetic macular edema analysis.- Representation and Reconstruction of Image-Based Structural Patterns of Glaucomatous Defects Using Only Two Latent Variables from a Variational Autoencoder.- Stacking Ensemble Learning in Deep Domain Adaptation for Ophthalmic Image Classification.- Attention Guided Slit Lamp Image Quality Assessment.- Robust Retinal Vessel Segmentation from a Data Augmentation Perspective.
Описание: This book constitutes the refereed proceedings of the 4th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2021, held on September 27, 2021, in conjunction with MICCAI 2021. The 17 papers presented in this book were carefully reviewed and selected from 27 submissions.
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